Аңдатпа
Introduction Ovarian cancer is the most fatal gynecologic cancer, contributing to approximately 207,000 deaths per year worldwide, largely due to late-stage diagnosis and considerable histopathological diversity across subtypes. Accurate subtype classification is clinically important for prognosis and treatment planning, but remains challenging due to high inter-class morphological similarity and the scarcity of adequately labeled histopathological datasets. Methods This study presents a novel attention module, Deep Differential Sparse Attention (DDSA), designed for multi-class classification of ovarian cancer subtypes in histopathology images. The module selectively amplifies discriminative tumor regions while suppressing irrelevant background features, thereby improving both feature representation and model interpretability. A dataset of 3,082 images spanning five subtypes i.e., Clear Cell Carcinoma, Endometrioid Carcinoma, High-Grade Serous Carcinoma, Low-Grade Serous Carcinoma, and Mucinous Carcinoma--was obtained from Kaggle. The training set was balanced to 5,940 images (1,188 per subtype) using RandomOverSampler, with on-the-fly data augmentation applied during training. DDSA was integrated into a DenseNet-based architecture, and diagnostic performance was evaluated using standard classification metrics, including precision, recall, F1-score, confusion matrices, and ROC curves. Results The proposed model achieved an overall classification accuracy of 92.53%, with a macro-average recall of 91.54% and a macro-average F1-score of 0.90. AUC values ranged from 0.92 to 0.99 across all five subtypes, indicating strong and consistent classification performance on the test set. Discussion These results suggest that attention-guided DenseNet architectures can enhance discriminative feature extraction and improve diagnostic accuracy for ovarian cancer subtype classification. The DDSA module's ability to highlight tumor-relevant regions also supports improved model interpretability, an important consideration for clinical adoption. While these findings are promising, further validation on larger, multi-institutional datasets is needed to confirm robustness and generalizability, and to assess the framework's readiness for clinical deployment.